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Background Patients with fractures are a common emergency presentation and may be misdiagnosed at radiologic imaging. An increasing number of studies apply artificial intelligence (AI) techniques to fracture detection as an adjunct to clinician diagnosis. Purpose To perform a systematic review and meta-analysis comparing the diagnostic performance in fracture detection between AI and clinicians in peer-reviewed publications and the gray literature (ie, articles published on preprint repositories). Materials and Methods A search of multiple electronic databases between January 2018 and July 2020 (updated June 2021) was performed that included any primary research studies that developed and/or validated AI for the purposes of fracture detection at any imaging modality and excluded studies that evaluated image segmentation algorithms. Meta-analysis with a hierarchical model to calculate pooled sensitivity and specificity was used. Risk of bias was assessed by using a modified Prediction Model Study Risk of Bias Assessment Tool, or PROBAST, checklist. Results Included for analysis were 42 studies, with 115 contingency tables extracted from 32 studies (55 061 images). Thirty-seven studies identified fractures on radiographs and five studies identified fractures on CT images. For internal validation test sets, the pooled sensitivity was 92% (95% CI: 88, 93) for AI and 91% (95% CI: 85, 95) for clinicians, and the pooled specificity was 91% (95% CI: 88, 93) for AI and 92% (95% CI: 89, 92) for clinicians. For external validation test sets, the pooled sensitivity was 91% (95% CI: 84, 95) for AI and 94% (95% CI: 90, 96) for clinicians, and the pooled specificity was 91% (95% CI: 81, 95) for AI and 94% (95% CI: 91, 95) for clinicians. There were no statistically significant differences between clinician and AI performance. There were 22 of 42 (52%) studies that were judged to have high risk of bias. Meta-regression identified multiple sources of heterogeneity in the data, including risk of bias and fracture type. Conclusion Artificial intelligence (AI) and clinicians had comparable reported diagnostic performance in fracture detection, suggesting that AI technology holds promise as a diagnostic adjunct in future clinical practice. Clinical trial registration no. CRD42020186641 © RSNA, 2022

Background This project was aimed at addressing junior doctors’ wellbeing in a district general hospital. It was initiated and delivered as part of a leadership initiative supported by the Wessex Deanery (Wessex Chief Resident Programme 2019–2020). Burn out rates among doctors range between 25% and 76%. The peer to peer support interventions are designed to improve coping and resilience among doctors and breaking the burnout cycle. The aim of this project was to evaluate the effect of peer to peer support groups in junior doctors’ morale and well-being. The peer-to-peer support provision as an intervention was introduced as an adjunct to routine trainee support measures already in place, i.e., educational programme and mentoring support. Method The peer-to-peer support meetings were organised through an opt in approach. All participants and the facilitator agreed on terms of reference for the meetings including confidentiality. Participants completed a feedback survey investigating how the sessions affected their clinical practice and well-being. No ethical approval was sought for this project as it was not considered to the needed. Results Twenty-three junior doctors participated in 20 sessions delivered over 6 months period. 25% completed a feedback survey. One third of the participants attended more than 70% of the sessions. Most participants scored 5 or more on the Likert scale for whether the sessions have changed their: sense of well-being at work, confidence levels, communication with patients and management of difficult situations. Recommendation This project provides further evidence that peer-to-peer support is beneficial for junior doctors’ wellbeing and morale. Positive participant feedback has encouraged other staff members to express interest in participation in a similar exercise. Dissemination plans are underway. It also provides further evidence that encouraging junior doctors to undertake leadership initiatives is transformative.